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Mobile Laser Scanning

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 65034

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Special Issue Editors

Prof. Harri Kaartinen

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Guest Editor

Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430 Masala, Finland
Interests: laser scanning; pointcloud technologies and applications; system performance; data quality

Dr. Antero Kukko

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Guest Editor

Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430 Masala, Finland
Interests: mobile laser scanning; LiDAR; GNSS; positioning; mapping; urban development; forestry; geography; planetary exploration

Special Issue Information

Dear Colleagues,

Since the previous mobile laser scanning and mobile mapping related Special Issue of Remote Sensing in 2013, the diversity of sensors suitable for mobile laser scanning has increased drastically, and integrated solutions have broken out in a rapid evolution cycle. A growing number of applications have emerged utilizing these new techniques. Currently, the use of MLS systems and data are commercialized by many companies and public corporations, but there are still many shortcomings to be solved so that the reliability and quality of the end products can be assured. Processing of the point cloud data collected with MLS systems efficiently and with methods fit to a particular task is of great importance to harvest the benefits of the technology. Therefore we see that a follow up for the 2013 Special Issue dedicated to these developments is well justified.

Prospective authors are invited to contribute to this Special Issue of Remote Sensing by submitting original manuscripts of their latest research results in the field of mobile laser scanning and 3D pointclouds acquired with such systems. Additionally, review contributions are welcomed. Contributions may be from, but not limited to:

Innovative multidisciplinary concepts and applications
Techniques for the fusion of MLS with other sensors
New methods in information extraction using 3D pointcloud data to all applications
MLS sensor and platform developments
Multispectral implementations of MLS
Calibration, accuracy and performance evaluations of the systems
Use of MLS in seamless indoor-outdoor 3D mapping
Affordable mobile mapping systems and applications
Improvement of georeferencing solutions in GNSS denied environments

Prof. Harri Kaartinen
Dr. Antero Kukko
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

mobile laser scanning
pointclouds
applications
sensor integration
accuracy
calibration

Published Papers (10 papers)

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Research

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33 pages, 17167 KiB

Open AccessArticle

High-Precision 3D Object Capturing with Static and Kinematic Terrestrial Laser Scanning in Industrial Applications—Approaches of Quality Assessment

by Ulrich Stenz, Jens Hartmann, Jens-André Paffenholz and Ingo Neumann

Remote Sens. 2020, 12(2), 290; https://doi.org/10.3390/rs12020290 - 15 Jan 2020

Cited by 16 | Viewed by 4846

Abstract

Terrestrial laser scanning is used in many disciplines of engineering. Examples include mobile mapping, architecture surveying, archaeology, as well as monitoring and surveillance measurements. For most of the mentioned applications, 3D object capturing in an accuracy range of several millimeters up to a few centimeters is sufficient. However, in engineering geodesy, particularly in industrial surveying or monitoring measurements, accuracies in a range of a few millimeters are required. Additional increased quality requirements apply to these applications. This paper focuses on the quality investigation of data captured with static and kinematic terrestrial laser scanning. For this purpose, suitable sensors, which are typically used in the approach of a multi-sensor-system, as well as the corresponding data capturing/acquisition strategies, are presented. The aim of such systems is a geometry- and surface-based analysis in an industrial environment with an accuracy of +/− 1–2 mm or better. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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23 pages, 19035 KiB

Open AccessArticle

Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds

by Zhenwei Shi, Zhizhong Kang, Yi Lin, Yu Liu and Wei Chen

Remote Sens. 2018, 10(12), 1891; https://doi.org/10.3390/rs10121891 - 27 Nov 2018

Cited by 26 | Viewed by 5354

Abstract

Mobile Laser Scanning (MLS) point cloud data contains rich three-dimensional (3D) information on road ancillary facilities such as street lamps, traffic signs and utility poles. Automatically recognizing such information from point cloud would provide benefits for road safety inspection, ancillary facilities management and so on, and can also provide basic information support for the construction of an information city. This paper presents a method for extracting and classifying pole-like objects (PLOs) from unstructured MLS point cloud data. Firstly, point cloud is preprocessed to remove outliers, downsample and filter ground points. Then, the PLOs are extracted from the point cloud by spatial independence analysis and cylindrical or linear feature detection. Finally, the PLOs are automatically classified by 3D shape matching. The method was tested based on two point clouds with different road environments. The completeness, correctness and overall accuracy were 92.7%, 97.4% and 92.3% respectively in Data I. For Data II, that provided by International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was also used to test the performance of the method, and the completeness, correctness and overall accuracy were 90.5%, 97.1% and 91.3%, respectively. Experimental results illustrate that the proposed method can effectively extract and classify PLOs accurately and effectively, which also shows great potential for further applications of MLS point cloud data. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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26 pages, 12512 KiB

Open AccessArticle

Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

by Ahmed Elseicy, Shayan Nikoohemat, Michael Peter and Sander Oude Elberink

Remote Sens. 2018, 10(11), 1815; https://doi.org/10.3390/rs10111815 - 15 Nov 2018

Cited by 15 | Viewed by 4903

Abstract

State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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23 pages, 12687 KiB

Open AccessArticle

Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

by Shayan Nikoohemat, Michael Peter, Sander Oude Elberink and George Vosselman

Remote Sens. 2018, 10(11), 1754; https://doi.org/10.3390/rs10111754 - 07 Nov 2018

Cited by 39 | Viewed by 4288

Abstract

The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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18 pages, 4517 KiB

Open AccessArticle

Panoramic Image and Three-Axis Laser Scanner Integrated Approach for Indoor 3D Mapping

by Pengcheng Zhao, Qingwu Hu, Shaohua Wang, Mingyao Ai and Qingzhou Mao

Remote Sens. 2018, 10(8), 1269; https://doi.org/10.3390/rs10081269 - 12 Aug 2018

Cited by 10 | Viewed by 5298

Abstract

High-precision indoor three-dimensional maps are a prerequisite for building information models, indoor location-based services, etc., but the indoor mapping solution is still in the stage of technological experiment and application scenario development. In this paper, indoor mapping equipment integrating a three-axis laser scanner and panoramic camera is designed, and the corresponding workflow and critical technologies are described. First, hardware design and software for controlling the operations and calibration of the spatial relationship between sensors are completed. Then, the trajectory of the carrier is evaluated by a simultaneous location and mapping framework, which includes reckoning of the real-time position and attitude of the carrier by a filter fusing the horizontally placed laser scanner data and inertial measurement data, as well as the global optimization by a closed-loop adjustment using a graph optimization algorithm. Finally, the 3D point clouds and panoramic images of the scene are reconstructed from two tilt-mounted laser scanners and the panoramic camera by synchronization of the position and attitude of the carrier. The experiment was carried out in a five-story library using the proposed prototype system; the results demonstrate accuracies of up to 3 cm for 2D maps, and up to 5 cm for 3D maps, and the produced point clouds and panoramic images can be utilized for modeling and further works related to large-scale indoor scenes. Therefore, the proposed system is an efficient and accurate solution for indoor 3D mapping. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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18 pages, 3770 KiB

Open AccessArticle

Extrinsic Calibration of 2D Laser Rangefinders Based on a Mobile Sphere

by Shoubin Chen, Jingbin Liu, Teng Wu, Wenchao Huang, Keke Liu, Deyu Yin, Xinlian Liang, Juha Hyyppä and Ruizhi Chen

Remote Sens. 2018, 10(8), 1176; https://doi.org/10.3390/rs10081176 - 25 Jul 2018

Cited by 19 | Viewed by 3860

Abstract

In the fields of autonomous vehicles, virtual reality and three-dimensional (3D) reconstruction, 2D laser rangefinders have been widely employed for different purposes, such as localization, mapping, and simultaneous location and mapping. However, the extrinsic calibration of multiple 2D laser rangefinders is a fundamental prerequisite for guaranteeing their performance. In contrast to existing calibration methods that rely on manual procedures or suffer from low accuracy, an automatic and high-accuracy solution is proposed in this paper for the extrinsic calibration of 2D laser rangefinders. In the proposed method, a mobile sphere is used as a calibration target, thereby allowing the automatic extrapolation of a spherical center and the automatic matching of corresponding points. Based on the error analysis, a matching machine of corresponding points with a low error is established with the restriction constraint of the scan circle radius, thereby achieving the goal of high-accuracy calibration. Experiments using the Hokuyo UTM-30LX sensor show that the method can increase the extrinsic orientation accuracy to a sensor intrinsic accuracy of 10 mm without requiring manual measurements or manual correspondence among sensor data. Therefore, the calibration method in this paper is automatic, highly accurate, and highly effective, and it meets the requirements of practical applications. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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16 pages, 59302 KiB

Open AccessArticle

Comparing Terrestrial Laser Scanning (TLS) and Wearable Laser Scanning (WLS) for Individual Tree Modeling at Plot Level

by Carlos Cabo, Susana Del Pozo, Pablo Rodríguez-Gonzálvez, Celestino Ordóñez and Diego González-Aguilera

Remote Sens. 2018, 10(4), 540; https://doi.org/10.3390/rs10040540 - 01 Apr 2018

Cited by 110 | Viewed by 9995

Abstract

This study presents a comparison between the use of wearable laser scanning (WLS) and terrestrial laser scanning (TLS) devices for automatic tree detection with an estimation of two dendrometric variables: diameter at breast height (DBH) and total tree height (TH). Operative processes for data collection and automatic forest inventory are described in detail. The approach used is based on the clustering of points belonging to each individual tree, the isolation of the trunks, the iterative fitting of circles for the DBH calculation and the computation of the TH of each tree. TLS and WLS point clouds were compared by the statistical analysis of both estimated forest dendrometric parameters and the possible presence of bias. Results show that the apparent differences in point density and relative precision between both 3D forest models do not affect tree detection and DBH estimation. Nevertheless, tree height estimation using WLS appears to be affected by the limited scanning range of the WLS used in this study. TH estimations for trees below a certain height are equivalent using WLS or TLS, whereas TH of taller trees is clearly underestimated using WLS. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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28 pages, 12819 KiB

Open AccessFeature PaperArticle

Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations

by Fashuai Li, Sander Oude Elberink and George Vosselman

Remote Sens. 2018, 10(4), 531; https://doi.org/10.3390/rs10040531 - 30 Mar 2018

Cited by 35 | Viewed by 6718

Abstract

Road furniture plays an important role in road safety. To enhance road safety, policies that encourage the road furniture inventory are prevalent in many countries. Such an inventory can be remarkably facilitated by the automatic recognition of road furniture. Current studies typically detect and classify road furniture as one single above-ground component only, which is inadequate for road furniture with multiple functions such as a streetlight with a traffic sign attached. Due to the recent developments in mobile laser scanners, more accurate data is available that allows for the segmentation of road furniture at a detailed level. In this paper, we propose an automatic framework to decompose road furniture into different components based on their spatial relations in a three-step procedure: first, pole-like road furniture are initially detected by removing ground points and an initial classification. Then, the road furniture is decomposed into poles and attachments. The result of the decomposition is taken as a feedback to remove spurious pole-like road furniture as a third step. If there are no poles extracted in the decomposition stage, these incorrectly detected pole-like road furniture—such as the pillars of buildings—will be removed from the detection list. We further propose a method to evaluate the results of the decomposition. Compared with our previous work, the performance of decomposition has been much improved. In our test sites, the correctness of detection is higher than 90% and the completeness is approximately 95%, showing that our procedure is competitive to state of the art methods in the field of pole-like road furniture detection. Compared to our previous work, the optimized decomposition improves the correctness by 7.3% and 18.4% in the respective test areas. In conclusion, we demonstrate that our method decomposes pole-like road furniture into poles and attachments with respect to their spatial relations, which is crucial for road furniture interpretation. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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Review

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21 pages, 3370 KiB

Open AccessReview

Autonomous Mobile Scanning Systems for the Digitization of Buildings: A Review

by Antonio Adán, Blanca Quintana and Samuel A. Prieto

Remote Sens. 2019, 11(3), 306; https://doi.org/10.3390/rs11030306 - 02 Feb 2019

Cited by 25 | Viewed by 4442

Abstract

Mobile scanning systems are being used more and more frequently in industry, construction, and artificial intelligent applications. More particularly, autonomous scanning plays an essential role in the field of the automatic creation of 3D models of building. This paper presents a critical review of current autonomous scanning systems, discussing essential aspects that determine the efficiency and applicability of a scanning system in real environments. Some important issues, such as data redundancy, occlusion, initial assumptions, the complexity of the scanned scene, and autonomy, are analysed in the first part of the document, while the second part discusses other important aspects, such as pre-processing, time requirements, evaluation, and opening detection. A set of representative autonomous systems is then chosen for comparison, and the aforementioned characteristics are shown together in several illustrative tables. Principal gaps, limitations, and future developments are presented in the last section. The paper provides the reader with a general view of the world of autonomous scanning and emphasizes the difficulties and challenges that new autonomous platforms should tackle in the future. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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33 pages, 24715 KiB

Open AccessReview

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

by Lingfei Ma, Ying Li, Jonathan Li, Cheng Wang, Ruisheng Wang and Michael A. Chapman

Remote Sens. 2018, 10(10), 1531; https://doi.org/10.3390/rs10101531 - 24 Sep 2018

Cited by 150 | Viewed by 14294

Abstract

The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services. Full article

(This article belongs to the Special Issue Mobile Laser Scanning)

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